AI Researcher/Prototyping Engineer

MIT Lincoln Laboratory•Lexington, MA

67d•$145,200 - $220,000

About The Position

The Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory is seeking motivated applicants to contribute to projects addressing a wide range of national needs in AI and ML. This group specializes in machine learning (ML) algorithms, technologies, and systems that extract and analyze information from multimedia data, and has expanded to include developing impactful ML solutions for cybersecurity. They are a recognized leader in both basic and applied AI/ML, shaping emerging AI fields and driving efforts in AI Assurance across the Department of Defense and the Intelligence Community. Their work focuses on operational relevance, designing and evaluating AI/ML systems using realistic datasets and metrics, and partnering with intelligence analysts and cyber operators for rapid technology transition. Assignments may include research on state-of-the-art algorithms in signal processing, natural language processing, graph analytics, or adversarial AI, applying innovative methods to real-world problems, and working in a collaborative, team-oriented development environment. This is an opportunity to apply skills to important technical challenges while learning from and contributing to a world-class research community.

Requirements

PhD in electrical engineering, computer science or other relevant discipline, or a Master's with 5 years of relevant experience.
Knowledge of artificial intelligence ideally with applications on multimedia, cyber security or adversarial/assurance experience.
Deep AI/ML experience – graduate-level (or professional) knowledge of state-of-the-art machine-learning, deep-learning, LLM/agentic AI, multimodal perception, graph analytics, adversarial/AI-assurance, or cyber-ML; ability to read, critique, and extend advanced research papers.
System analysis & problem decomposition – can translate high-level end-state requirements into a clear set of research tasks, define quantitative success metrics and measures, and produce a task-breakdown structure that aligns with program/project milestones. Adept at formulating hypothesis, designing controlled experiments, and drawing data-driven conclusions on novel problems.
Operate within a rapid research-to-prototype pipeline – demonstrable ability to turn novel algorithms and approaches into robust, reproduceable prototypes in tight timescales, around iterative refinement.
Agentic-AI framework experience – hands-on work with LangChain or comparable toolkits; able to design, implement, and evaluate end-to-end LLM-driven agents.
Advanced Python & ML libraries – expert-level proficiency in Python and deep-learning frameworks (PyTorch, TensorFlow, etc.), plus Huggingface ecosystem, NumPy/pandas/Scipy, etc.
Software-engineering best practices – strong Git workflow (branching, pull-requests, code reviews), continuous-integration testing, environment reproducibility (e.g., conda, virtualenv). Ability to document code, write reproducible experiment notebooks, and maintain versioned releases.
Team and Project leadership – experience leading small research teams, assigning tasks, tracking progress, removing technical blockers, and mentoring junior staff.
Stakeholder communication – strong written and oral skills for prepping technical briefings, composing white-papers, demo presentations, and funding proposals; able to convey complex research outcomes to sponsors, senior leadership, and external partners.
Detail-oriented, able to multi-task, with the ability to work autonomously, set technical direction, and operate with minimal supervision.
Current TS/SCI clearance or willingness to obtain one.
Selected candidate will be subject to a pre-employment background investigation and must be able to obtain and maintain a Secret level DoD security clearance.

Responsibilities

Design, program, and architect advanced ML methods.
Develop algorithms in speech, natural language processing, multimedia, cyber, and graph analytics.
Publish and present research at premier conferences.
Research on state-of-the-art algorithms in signal processing, natural language processing, graph analytics, or adversarial AI.
Applying innovative methods to challenging, real-world problems.
Working in a collaborative, team-oriented development environment.